US Inflation, as measured by Bureau of Labor Statistics (BLS)
This is inspired by a Jonathan Regenstein twitter post from Dec 6th, 2021.
I want to update the analysis with latest data + make some modifications to his original charts.
- Jonathan’s twitter post: https://twitter.com/jkregenstein/status/1467962068863275011?s=20
- Jonathan’s original github code: https://gist.github.com/jkr216/0c6463e99dc54f323ed4976392ce3c57
Section 1: get & prepare data
Let’s use the {tidyquant} package to access the BLS data in a tidy format. For starters, lets make sure that the data from {tidyquant} is the same as the official BLS press release.
The latest BLS press releases: https://www.bls.gov/news.release/cpi.nr0.htm
Most commonly quoted figure from that press release appears to the 8.5% year-over-year increase in “All Items”
# Section 1.1: CPI Consumer Price Index for All Urban Consumers: All Items in U.S. City Average
CPIAUCSL_YY12M_SA <- tq_get("CPIAUCSL", get= "economic.data", from = "2018-01-01") %>%
summarise_by_time(
.date_var = date,
.by = "month",
CPI_allitems_monthly = mean(price, na.rm = T)) %>%
mutate(CPI_YY_12M_SA = YY(CPI_allitems_monthly)*100) %>%
rename(`Reported Inflation` = CPI_YY_12M_SA) %>%
na.omit()
tail(CPIAUCSL_YY12M_SA, 3)
# A tibble: 3 x 3
date CPI_allitems_monthly `Reported Inflation`
<date> <dbl> <dbl>
1 2022-02-01 284. 7.91
2 2022-03-01 288. 8.56
3 2022-04-01 289. 8.22Hmm, Looks like there is a very small difference between these numbers and the Mar’22 release from BLS. The script above returns 8.56% (rounding to 8.6%), whereas the BLS reported 8.5%
A closer read of their press release reveals that they are using the not seasonally adjusted figures for their 12 Month Change calculations. Let’s modify the above code - replace the FRED ID from “CPIAUCSL” to “CPIAUCNS”
# Section 1.2: CPI Consumer Price Index for All Urban Consumers: All Items in U.S. City Average
# Not Seasonally Adjusted
CPIAUCSL_YY12M_NSA <- tq_get("CPIAUCNS", get= "economic.data", from = "2018-01-01") %>%
summarise_by_time(
.date_var = date,
.by = "month",
CPI_allitems_monthly = mean(price, na.rm = T)) %>%
mutate(CPI_YY_12M_NSA = YY(CPI_allitems_monthly)*100) %>%
rename(`Reported Inflation` = CPI_YY_12M_NSA) %>%
na.omit()
tail(CPIAUCSL_YY12M_NSA, 3)
# A tibble: 3 x 3
date CPI_allitems_monthly `Reported Inflation`
<date> <dbl> <dbl>
1 2022-02-01 284. 7.87
2 2022-03-01 288. 8.54
3 2022-04-01 289. 8.26The latest data point (8.5% for March 2022) in my data frame, matches the BLS press release. Now, on to the rest of the exploration.
Section 2.1: Visualize Reported Inflation
First chart, a look at inflation from Jan 2019 to March 2022.
CPIAUCSL_chart <- CPIAUCSL_YY12M_NSA %>%
ggplot(aes(x=date, y=`Reported Inflation`)) +
geom_line() +
geom_point() +
geom_hline(yintercept = 0) +
scale_y_continuous(labels = scales::number_format(accuracy = 0.1),
breaks = pretty_breaks(10)) +
scale_x_date(date_breaks = "1 month",
date_labels = "%m-%y") +
labs(title = "Reported Inflation",
y = "Inflation (%)", x = "Month") +
theme(axis.text.x=element_text(angle=45, hjust=1, size = 4))
Interactive chart via {plotly}
Notable trends
* Inflation breaks past 3% around April 2021
* Briefly plateau around 5.5% between June-September 2021
* Sharp upward trajectory from Oct 2021 to present
* Latest print of 8.5% is a 40 year high
All of these recent trends are causing alarm among inflation watchers.
Section 2.2: Visualize Expected Future Inflation
Many inflation observers are keeping a close eye on not just today’s inflation, but expected future inflation. A good measure is the “5-Year Breakeven Inflation Rate”. Definition from the Federal Reserve: “The breakeven inflation rate represents a measure of expected inflation derived from 5-Year Treasury Constant Maturity Securities (BC_5YEAR) and 5-Year Treasury Inflation-Indexed Constant Maturity Securities (TC_5YEAR). The latest value implies what market participants expect inflation to be in the next 5 years, on average.” Lets use the FRED ID “T5YIE” to access this from {tidyquant}.
T5YIE_monthly <- tq_get("T5YIE", get= "economic.data", from = "2018-01-01") %>%
mutate(Percent = price/100) %>%
summarise_by_time(
.date_var = date,
.by = "month",
expected_five_year = mean(Percent, na.rm = T)*100) %>%
rename(`Expected Inflation` = expected_five_year)
T5YIE_chart <- T5YIE_monthly %>%
ggplot(aes(x=date, y=`Expected Inflation`)) +
geom_line() +
geom_point() +
geom_hline(yintercept = 0) +
scale_y_continuous(labels = scales::number_format(accuracy = 0.1),
breaks = pretty_breaks(10)) +
scale_x_date(date_breaks = "1 month",
date_labels = "%m-%y") +
labs(title = "Expected Inflation",
y = "Inflation (%)", x = "Month") +
theme(axis.text.x=element_text(angle=90, hjust=1, size = 4))
T5YIE_plotly <- ggplotly(T5YIE_chart) %>%
layout(title = list(text = paste0('Expected Inflation',
'<br>',
'<sup>',
'Source: Fed Reserve, Viz by @TidySigma',
'</sup>'))) %>%
config(displayModeBar = FALSE)
T5YIE_plotly
Similar to reported inflation, today’s expectation for inflation 5 years from now is on a similar upward trajectory.
To read the above chart:
* As of March 2022, the market expected March 2027 inflation to be around 3.4%
* Good news: market expects inflation to come down from today 8.5% down to about 3.4% 5 years from now
* Bad news: inflation is expected to be elevated (i.e. beyond the “normal” ~2% level)
Section 2.3: combine reported inflation data with expected inflation
Looking at these two trends side-by-side gives us an interesting view of what is going on.
In addition to plotting these two trends, I like Jonathan’s idea of subtracting expected minus actual to get to an “Unexpected Inflation” figure.
df <- left_join(T5YIE_monthly, CPIAUCSL_YY12M_NSA, by="date") %>%
select(date, `Expected Inflation`, `Reported Inflation`) %>%
mutate(`Unexpected Inflation` = `Reported Inflation` - `Expected Inflation`) %>%
filter(date > "2019-03-01") %>%
mutate(across(`Expected Inflation`:`Unexpected Inflation`, ~round(.x,3)))
chart_plotly <- df %>%
plot_ly(x=~date, y=~`Reported Inflation`, type='scatter', mode='lines+markers', name = "Reported") %>%
add_trace(x=~date, y=~`Expected Inflation`, type='scatter', mode='lines+markers', name = "Expected") %>%
add_trace(x=~date, y=~`Unexpected Inflation`, type = 'bar', name = "Unexpected",
opacity=0.25) %>%
layout(title = list(text = paste0('Inflation: Reported, Expected & Unexpected',
'<br>',
'<sup>',
'Sources: BLS, Federal Reserve, Viz by @TidySigma',
'</sup>') ),
plot_bgcolor = "#e5ecf6",
yaxis = list(title = 'Inflation Percent (%)', ticksuffix = "%"),
xaxis = list(title = 'Month', type = 'date',
tickformat = "%m-%y", tickangle=-90, tickfont = list(size = 4),
range = list("2019-03-01", "2022-05-01"),
showgrid = T, dtick="M1")) %>%
config(displayModeBar = FALSE)
chart_plotly
Summary of findings
* As of a few months ago, a lot of market watchers were not sure sure if inflation was going to continue to go up, or if things were going to be transitory.
* Clearly inflation has picked up in recent months, and this is cause for concern.
* The gap between reported inflation and 5 year expectation of inflation (the green bars) remains large, so market participants expect inflation to eventually come down.
* It is important to keep an eye on expected inflation, hopefully this will go down to a less alarming level soon.
For any questions, comments, bug notifications, etc; please contact me via Twitter @TidySigma
End